In Vivo Computational Strategy for Tumor Targeting in Co-Associated Biological Landscapes

Recently, a novel framework of in vivo computation has been proposed by modeling the tumor targetingproblem as a natural computation problem. The tumor-triggered biological gradient field (BGF) which provides targeting information for the nanorobots is viewed as the objective function to be optimized. The previous work focuses on the scenario of single BGF, which is interpreted as a uni-objective optimization problem. However, in real-life scenarios, various BGFs will be induced by the arising of a tumor lesion because of the variations of different kinds of biological information around the lesion (e.g., blood velocity, pH, oxygen, glucose, lactate, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm H^{+}$</tex-math></inline-formula> ions). It is plausible to utilize BGF information as much as possible to target the tumor efficiently and robustly. Thus, we propose a BGF selector, which consists of a neural network “VisionaryNet”, a swarm intelligence algorithm, and a weak priority evolution strategy (WP-ES) in this article. Various artificial BGF landscapes are used to train the proposed VisionaryNet, which is employed to choose the alternative BGFs combined with several on-line estimated features during each iterative step. To demonstrate the effectiveness of the proposed BGF selector, a random selection approach is used as the benchmark. Comprehensive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in silico</i> experiments are carried out by taking into consideration the in vivo constraints of the nanobiosensing process. Furthermore, the correlation between the number of employed BGFs and the targeting result is investigated as the increasing of the number of BGFs will lead to excessive computation, which is adverse to the computational accuracy.